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Medical Science Monitor: International Medical Journal of Experimental and Clinical Research logoLink to Medical Science Monitor: International Medical Journal of Experimental and Clinical Research
. 2024 Jun 15;30:e943369-1–e943369-24. doi: 10.12659/MSM.943369

Comprehensive Analysis of Sphingolipid Metabolism-Related Genes in Osteoarthritic Diagnosis and Synovial Immune Dysregulation

Zheng Zhu 1,D,E,*, Bizhi Tu 1,B,*, Run Fang 1,C, Jun Tong 1,D, Yulong Liu 1,F, Rende Ning 1,A,✉
PMCID: PMC11186385  PMID: 38877693

Abstract

Background

Osteoarthritis (OA) is a chronic degenerative disease characterized by synovitis and has been implicated in sphingolipid metabolism disorder. However, the role of sphingolipid metabolism pathway (SMP)-related genes in the occurrence of OA and synovial immune dysregulation remains unclear.

Material/Methods

In this study, we obtained synovium-related databases from GEO (n=40 for both healthy controls and OA) and analyzed the expression levels of SMP-related genes. Using 2 algorithms, we identified hub genes and developed a diagnostic model incorporating these hub genes to predict the occurrence of OA. Subsequently, the hub genes were further validated in peripheral blood samples from OA patients. Additionally, CIBERSORT and MCP-counter analyses were employed to explore the correlation between hub genes and immune dysregulation in OA synovium. WGCNA was used to determine enriched modules in different clusters.

Results

Overall, the expression levels of SMP genes were upregulated in OA synovium. We identified 6 hub genes of SMP and constructed an excellent diagnostic model (AUC=0.976). The expression of re-confirmed hub genes showed associations with immune-related cell infiltration and levels of inflammatory cytokines. Furthermore, we observed heterogeneity in the expression patterns of hub genes across different clusters of OA. Notably, older patients displayed increased susceptibility to elevated levels of pain-related inflammatory cytokines and infiltration of immune cells.

Conclusions

The SMP-related hub genes have the potential to serve as diagnostic markers for OA patients. Moreover, the 4 hub genes of SMP demonstrate wide participation in immune dysregulation in OA synovium. The activation of different pathways is observed among different populations of patients with OA.

Keywords: Biomarkers; Immune System Phenomena; Lipid Metabolism Disorders; Osteoarthritis, Knee; Sphingolipids

Introduction

Osteoarthritis (OA) is a chronic degenerative disease that affects joint function. Its global prevalence rate is estimated to be 6–32%, with the elderly population being most affected [1]. Various risk factors contribute to the development of OA, including aging, excessive physical labor, dyslipidemia, and metabolic syndrome [2,3]. OA is characterized by progressive cartilage degeneration, subchondral sclerosis, synovial inflammation, meniscus degeneration, and osteophyte formation [4–6]. Clinical manifestations of OA include knee dysfunction, stiffness, and pain, which impose physical, psychological, and socio-economic burdens, particularly on the elderly [7]. Previous studies have highlighted the significant role of the synovium in OA development, as joint degeneration occurs following apparent pathological alterations in synovial inflammatory responses [8,9]. Osteoarthritic synovial fibroblasts help support immune cell activation [10] and secrete joint-damaged cytokines such as IL-6, TNF, and MMP3 [11]. The inflammatory response aroused by synovitis triggers local infiltration of macrophages, T cells, and B cells [12], which in turn lead to articular cartilage destruction and accelerated joint degeneration through inflammatory cascades [13,14]. Reducing synovial inflammation can help alleviate joint pain, or even reverse articular structural remodeling [15]. There is an urgent need to explore the underlying cellular mechanism of synovitis and methods of protecting the OA synovium from damaged cytokines in people with OA [16].

There are currently several therapies for OA, including limitation of activity, alleviation of pain, and mitigation of cartilage injury in the early stage, for delaying OA progression [17]. As joint degeneration progresses, many patients eventually receive knee replacement [18]. Thus, adequate diagnosis of OA becomes very important. However, the confirmation of OA depends on imaging data and patient symptoms [19,20], and timely diagnosis of OA is difficult due to lack of specific clinical manifestations. The effective recognition of OA is key to preventing the loss of articular cartilage and OA progression in joints [17, 21], and numerous studies have unveiled early diagnostic biomarkers for OA [22,23], but the clinical utilization of these biomarkers remains a challenge, underscoring the need for further exploration of potential candidate markers for clinical application.

Lipid metabolism disorder has been demonstrated to be important in OA pathogenesis, with significant differences in lipid metabolites observed between the synovial fluid of people with OA versus those without OA [24,25]. Specifically, sphingolipids have been found to be upregulated in OA and play a protective role in inhibiting immune infiltration and attenuating synovitis [26,27]. Therefore, sphingolipid metabolism pathways (SMP) may be involved in regulating synovitis in OA. However, the exact impact of SMP gene expression on synovitis has not been reported, and it remains unclear whether SMP genes can be applied to diagnosis of OA. In this study, we identified 4 hub genes associated with SMP in OA synovium. These hub genes have the potential to serve as diagnostic markers for OA and play a crucial role in immune cell infiltration and inflammatory factors. A deeper understanding of the molecular function of SMP genes in osteoarthritic synovium holds promise for exploring novel diagnostic biomarkers for OA and guiding future research endeavors.

Material and Methods

Preprocessing of Raw Data Source

The workflow chart of this study is shown in Figure 1. Gene sets expression (GSE) profiles containing the gene expression information of healthy (CT) and OA synovium (GSE29746, GSE12021, GSE55235, GSE55457) were downloaded from the GEO online database with search term “osteoarthritis AND (synovitis OR synovium)”. Then, the batch effects were removed with the “sva” package [28], and we obtained an integrated dataset including 40 OA and 40 healthy samples. Gene annotation file profiles, which used for converting gene IDs within included transcriptome data into gene symbols, were downloaded from the GPL96 (GSE55235, GSE55457, and GSE12021) and GPL4133 (GSE29746) platforms. The genes of SMP were acquired from Reactome (https://reactome.org/). The demographic information of these patients is shown in Table 1.

Figure 1.

Figure 1

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Work flow chart. Drawn by AI (Adobe Illustrator CC2021, v25.0.0.60 for Mac version, Adobe Corporation).

Table 1.

The clinic information of included patients with OA in 5 online databases.

Database ID Sample ID Organism Age (year) Sex Group
GSE29746 GSM737470 Homo sapiens 21 Male CT
GSE29746 GSM737471 Homo sapiens 40 Male CT
GSE29746 GSM737472 Homo sapiens 58 Female CT
GSE29746 GSM737473 Homo sapiens 68 Female CT
GSE29746 GSM737474 Homo sapiens 79 Male CT
GSE29746 GSM737475 Homo sapiens 46 Female CT
GSE29746 GSM737476 Homo sapiens 84 Male CT
GSE29746 GSM737477 Homo sapiens 57 Male CT
GSE29746 GSM737478 Homo sapiens 58 Female CT
GSE29746 GSM737479 Homo sapiens 56 Female CT
GSE29746 GSM737480 Homo sapiens 82 Female CT
GSE29746 GSM737481 Homo sapiens 80 Male OA
GSE29746 GSM737482 Homo sapiens 75 Male OA
GSE29746 GSM737483 Homo sapiens 66 Female OA
GSE29746 GSM737484 Homo sapiens 80 Male OA
GSE29746 GSM737485 Homo sapiens 71 Female OA
GSE29746 GSM737486 Homo sapiens 56 Female OA
GSE29746 GSM737487 Homo sapiens 73 Female OA
GSE29746 GSM737488 Homo sapiens 76 Male OA
GSE29746 GSM737489 Homo sapiens 74 Female OA
GSE29746 GSM737490 Homo sapiens 80 Male OA
GSE29746 GSM737491 Homo sapiens 72 Female OA
GSE12021 GSM302859 Homo sapiens 61 Male CT
GSE12021 GSM302864 Homo sapiens 64 Male CT
GSE12021 GSM302866 Homo sapiens 88 Female CT
GSE12021 GSM302870 Homo sapiens 65 Male CT
GSE12021 GSM303522 Homo sapiens 53 Male CT
GSE12021 GSM303523 Homo sapiens 29 Female CT
GSE12021 GSM303525 Homo sapiens 17 Male CT
GSE12021 GSM303531 Homo sapiens 39 Male CT
GSE12021 GSM303533 Homo sapiens 36 Male CT
GSE12021 GSM302880 Homo sapiens 71 Female OA
GSE12021 GSM302930 Homo sapiens 76 Female OA
GSE12021 GSM303326 Homo sapiens 61 Female OA
GSE12021 GSM303341 Homo sapiens 75 Female OA
GSE12021 GSM303356 Homo sapiens 78 Male OA
GSE12021 GSM303358 Homo sapiens 64 Male OA
GSE12021 GSM303360 Homo sapiens 71 Female OA
GSE12021 GSM303362 Homo sapiens 80 Female OA
GSE12021 GSM303370 Homo sapiens 66 Female OA
GSE55235 GSM1332201 Homo sapiens NA NA CT
GSE55235 GSM1332202 Homo sapiens NA NA CT
GSE55235 GSM1332203 Homo sapiens NA NA CT
GSE55235 GSM1332204 Homo sapiens NA NA CT
GSE55235 GSM1332205 Homo sapiens NA NA CT
GSE55235 GSM1332206 Homo sapiens NA NA CT
GSE55235 GSM1332207 Homo sapiens NA NA CT
GSE55235 GSM1332208 Homo sapiens NA NA CT
GSE55235 GSM1332209 Homo sapiens NA NA CT
GSE55235 GSM1332210 Homo sapiens NA NA CT
GSE55235 GSM1332211 Homo sapiens NA NA OA
GSE55235 GSM1332212 Homo sapiens NA NA OA
GSE55235 GSM1332213 Homo sapiens NA NA OA
GSE55235 GSM1332214 Homo sapiens NA NA OA
GSE55235 GSM1332215 Homo sapiens NA NA OA
GSE55235 GSM1332216 Homo sapiens NA NA OA
GSE55235 GSM1332217 Homo sapiens NA NA OA
GSE55235 GSM1332218 Homo sapiens NA NA OA
GSE55235 GSM1332219 Homo sapiens NA NA OA
GSE55235 GSM1332220 Homo sapiens NA NA OA
GSE55457 GSM1337304 Homo sapiens 61 Male CT
GSE55457 GSM1337305 Homo sapiens 64 Male CT
GSE55457 GSM1337306 Homo sapiens 78 Female CT
GSE55457 GSM1337307 Homo sapiens 65 Male CT
GSE55457 GSM1337308 Homo sapiens 53 Male CT
GSE55457 GSM1337309 Homo sapiens 68 Male CT
GSE55457 GSM1337310 Homo sapiens 29 Female CT
GSE55457 GSM1337311 Homo sapiens 17 Male CT
GSE55457 GSM1337312 Homo sapiens 39 Male CT
GSE55457 GSM1337313 Homo sapiens 36 Male CT
GSE55457 GSM1337327 Homo sapiens 77 Female OA
GSE55457 GSM1337328 Homo sapiens 71 Female OA
GSE55457 GSM1337329 Homo sapiens 76 Female OA
GSE55457 GSM1337330 Homo sapiens 61 Female OA
GSE55457 GSM1337331 Homo sapiens 75 Female OA
GSE55457 GSM1337332 Homo sapiens 78 Male OA
GSE55457 GSM1337333 Homo sapiens 69 Male OA
GSE55457 GSM1337334 Homo sapiens 71 Female OA
GSE55457 GSM1337335 Homo sapiens 80 Female OA
GSE55457 GSM1337336 Homo sapiens 66 Female OA
GSE63359 GSM1546587 Homo sapiens 46 Female CT
GSE63359 GSM1546588 Homo sapiens 54 Female CT
GSE63359 GSM1546589 Homo sapiens 54 Female CT
GSE63359 GSM1546590 Homo sapiens 56 Male CT
GSE63359 GSM1546591 Homo sapiens 54 Female CT
GSE63359 GSM1546592 Homo sapiens 52 Male CT
GSE63359 GSM1546593 Homo sapiens 62 Female CT
GSE63359 GSM1546594 Homo sapiens 49 Female CT
GSE63359 GSM1546595 Homo sapiens 55 Female CT
GSE63359 GSM1546596 Homo sapiens 49 Female CT
GSE63359 GSM1546597 Homo sapiens 56 Female CT
GSE63359 GSM1546598 Homo sapiens 57 Male CT
GSE63359 GSM1546599 Homo sapiens 49 Female CT
GSE63359 GSM1546600 Homo sapiens 58 Female CT
GSE63359 GSM1546601 Homo sapiens 66 Female CT
GSE63359 GSM1546602 Homo sapiens 31 Female CT
GSE63359 GSM1546603 Homo sapiens 58 Female CT
GSE63359 GSM1546604 Homo sapiens 48 Male CT
GSE63359 GSM1546605 Homo sapiens 65 Male CT
GSE63359 GSM1546606 Homo sapiens 50 Female CT
GSE63359 GSM1546607 Homo sapiens 52 Male CT
GSE63359 GSM1546608 Homo sapiens 87 Female CT
GSE63359 GSM1546609 Homo sapiens 50 Male CT
GSE63359 GSM1546610 Homo sapiens 49 Female CT
GSE63359 GSM1546611 Homo sapiens 53 Female CT
GSE63359 GSM1546612 Homo sapiens 59 Female
GSE63359 GSM1546613 Homo sapiens 69 Female OA
GSE63359 GSM1546614 Homo sapiens 74 Male OA
GSE63359 GSM1546615 Homo sapiens 53 Female OA
GSE63359 GSM1546616 Homo sapiens 64 Female OA
GSE63359 GSM1546617 Homo sapiens 74 Female OA
GSE63359 GSM1546618 Homo sapiens 52 Female OA
GSE63359 GSM1546619 Homo sapiens 70 Female OA
GSE63359 GSM1546620 Homo sapiens 80 Male OA
GSE63359 GSM1546621 Homo sapiens 83 Female OA
GSE63359 GSM1546622 Homo sapiens 77 Male OA
GSE63359 GSM1546623 Homo sapiens 49 Female OA
GSE63359 GSM1546624 Homo sapiens 61 Female OA
GSE63359 GSM1546625 Homo sapiens 51 Female OA
GSE63359 GSM1546626 Homo sapiens 75 Female OA
GSE63359 GSM1546627 Homo sapiens 72 Male OA
GSE63359 GSM1546628 Homo sapiens 84 Female OA
GSE63359 GSM1546629 Homo sapiens 72 Female OA
GSE63359 GSM1546630 Homo sapiens 66 Female OA
GSE63359 GSM1546631 Homo sapiens 64 Female OA
GSE63359 GSM1546632 Homo sapiens 56 Male OA
GSE63359 GSM1546633 Homo sapiens 75 Female OA
GSE63359 GSM1546634 Homo sapiens 56 Female OA
GSE63359 GSM1546635 Homo sapiens 79 Female OA
GSE63359 GSM1546636 Homo sapiens 72 Male OA
GSE63359 GSM1546637 Homo sapiens 56 Male OA
GSE63359 GSM1546638 Homo sapiens 65 Female OA
GSE63359 GSM1546639 Homo sapiens 53 Female OA
GSE63359 GSM1546640 Homo sapiens 47 Female OA
GSE63359 GSM1546641 Homo sapiens 57 Male OA
GSE63359 GSM1546642 Homo sapiens 50 Male OA
GSE63359 GSM1546643 Homo sapiens 50 Female OA
GSE63359 GSM1546644 Homo sapiens 78 Female OA
GSE63359 GSM1546645 Homo sapiens 76 Female OA
GSE63359 GSM1546646 Homo sapiens 52 Female OA
GSE63359 GSM1546647 Homo sapiens 80 Male OA
GSE63359 GSM1546648 Homo sapiens 56 Female OA
GSE63359 GSM1546649 Homo sapiens 68 Female OA
GSE63359 GSM1546650 Homo sapiens 62 Male OA
GSE63359 GSM1546651 Homo sapiens 78 Female OA
GSE63359 GSM1546652 Homo sapiens 73 Female OA
GSE63359 GSM1546653 Homo sapiens 62 Female OA
GSE63359 GSM1546654 Homo sapiens 57 Male OA
GSE63359 GSM1546655 Homo sapiens 74 Male OA
GSE63359 GSM1546656 Homo sapiens 74 Male OA
GSE63359 GSM1546657 Homo sapiens 69 Female OA
GSE63359 GSM1546658 Homo sapiens 54 Female OA
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Identification of SMP-Related Differential Expression Genes (DEGs) in OA Synovium

Using the “limma” package in R software, the differential expression analysis was used to explore the DEGs between OA and healthy synovium in the integrated dataset. Using Venn analysis, the SMP-related DEGs were identified by overlapping the genes of SMP and DEGs. The “heatmap” and “ggplot2” packages in R software were used to draw the heatmap and volcano plots, and the “ggpubr” package was used to create boxplots.

Identification of SMP-Related Hub Genes in OA Synovium

Using the “randomForest” package, we conducted random forest classifiers consisting of 1000 decision trees and then validated the 10-fold cross. The SMP-related genes were analyzed with the random forest function in the “randomForest” package and the “forestplot” package was used to generate a forest plot. The significance of each SMP-related gene was evaluated by importance using random forest analysis as previously described [29], and the top 10 were reserved. SVM recursive feature elimination (SVM-RFE) was employed to discard the relatively low predictive feature in each iteration [30]. Hence, SMP genes were ranked from most to least important, and the top 10 were acquired for further analysis. Then, the top 10 SMP-related genes that were acquired separately by random forest and SVM-RFE analysis were applied to Venn analysis, and the SMP-related hub genes were further identified in the peripheral blood samples of OA (GSE63359).

Construction of OA Prediction Model

Multi-factor logistic regression analysis was used to evaluate the diagnostic model. We calculated the area under the receiver operator characteristic (ROC) curve to predict whether the identified hub genes have diagnostic value in OA. According to the results of multi-factor analysis and clinical information, a predictive nomogram was constructed using the “regplot” and “rms” packages in R software [31]. Calibration plots and decision curve analysis (DCA) were used to assess the applicability of the constructed nomogram.

Correlation Analysis Between the Expression of SMP-Related Hub Genes and the Immune Microenvironment

Using the “relative” and “absolute” methods available on the CIBERSORT (https://cibersortx.stanford.edu/), the local abundance of immune cells in OA synovium was analyzed by the CIBERSORT module. Gene expression deconvolution was performed on RNAseq TPM level data of clinical samples. Based on CIBERSORT and MCP-counter analysis, we derived the relationship between SMP-related hub genes and the abundance of immune-related cells.

Consensus Clustering and WGCNA Analysis

“ConsensusClusterPlus” package was utilized to conduct the consensus cluster analysis. The median absolute deviation was used to measure the top 5000 most variable genes applied for samples clustered. The cluster of OA samples and identification of the optimal number of clusters was conducted through unsupervised consensus clustering. Among each 1000 resampling iterations in consensus clustering, we applied ward linkage as an agglomeration method and Euclidean distance as a distance measure to perform hierarchical clustering. The gene sets with similar mRNA transcription models across OA synovium samples were weighted by WGCN analysis [32]. We first normalized the expression of OA synovium genes. Using a power adjacency function, we obtained connection strengths between any 2 genes by converting this matrix into an adjacency matrix. The scale-free topology (SFT) criterion was used to choose the parameter (soft threshold) for adjacency, where the scale-free topology is necessary to construct the network. Based on the SFT criterion recommendation, the optimal threshold parameter value was accepted due to its model-fit saturation of more than 0.85. Then, the optimal topology as soft-threshold power was identified by the “pickSoftThreshold” function. The module was determined using the “tree” method with “deepSplit” to identify modules with no less than 10 genes. Each modular feature was represented by an eigengene generated using the DynamicTreeCut algorithm in the context of WGCNA, as previously described [33]. Default values of WGCNA were consistently applied throughout the process unless stated otherwise. The co-expression modules that enriched for “Biological Process” in Gene Ontology (GO) enrichment analysis were described using Gorilla [34], which is based on pre-inputted gene sets into WGCNA.

GO/KEGG Enrichment Analysis

The genes that were most obviously different in the WGCNA model were utilized to perform GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis based on the DAVID database (https://david.ncifcrf.gov/tools.jsp). All pathways with statistical significance (P<0.05) were retained.

Statistical Analysis

All data analysis and processing were performed in R (version 4.2.1). SPSS software (version 23.0) was used for statistical analysis, which began by thoroughly evaluating normality and homoscedasticity to determine the appropriate test method, whether parametric or non-parametric. Normally distributed data underwent the two-tailed unpaired t test, whereas non-normally distributed data or cases not meeting t test assumptions were subjected to the Wilcoxon’s rank sum test between the CT and OA groups. All P values were 2-sided, and P<0.05 was considered statistically significant.

Results

Identification of 6 SMP-Related Hub Genes in OA Aynovium

After removing the batch effects, we performed the differential expression analysis in the integrated database (Figure 2A). We found that the SMP-related genes were expressed at higher levels in the OA group than in the CT group (Figure 2B, P<0.05). The 11 314 DEGs were detected by differential expression analysis between OA and the healthy group. Then, 48 SMP-related DEGs were obtained through overlapping the 11 314 DEGs and 93 SMP genes. A volcano and a box plot presented the level of 48 SMP-related DEGs (Figure 2C). The heatmap presents the expression heterogeneity of 48 SMP-related DEGs between healthy and OA synovium (Figure 2C). The 48 SMP-related DEGs were ranked by SVM-RFE and random forest analysis (Figure 2D). Then, 6 candidate hub SMP-related genes in OA synovium were obtained by intersecting the top 10 genes ranked by SVM-RFE and random forest analysis (Figure 3A).

Figure 2.

Figure 2

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Differential expression of genes of the sphingolipid metabolism pathways (SMP). (A) Principal component analysis (PCA) plot shows the batch effect was removed among the 4 obtained datasets. (B) Upregulated expression of sphingolipid metabolism in OA synovium. (C) Volcano plot, box plot, and heat map of differentially expressed SMP genes between OA and healthy (CT) synovium. (D) The rank of SMP-related genes by the random forest analysis and SVM-RFE algorithm. * P<0.05. Drawn by AI (Adobe Illustrator CC2021, v25.0.0.60 for Mac version, Adobe Corporation) and R (version 4.2.1, JJ Allaire Company).

Figure 3.

Figure 3

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Prediction model for OA was constructed by identified hub genes related to sphingolipid metabolism. (A) Six candidate hub genes were identified by random forest and SVM-RFE analysis. (B) Nomogram of 6 candidate hub genes in the diagnosis of OA patients. (C) Receiver operating characteristic (ROC) curve and its verification of repeated sampling, as well as calibration curve indicates the excellence and robustness of model in predicting the OA occurrence. (D) Model evaluation curves: A diagnostic model containing multiple nominated hub genes makes patients more profitable than a single gene. * P<0.05, ** P<0.01. Drawn by AI (Adobe Illustrator CC2021, v25.0.0.60 for Mac version, Adobe Corporation) and R (version 4.2.1, JJ Allaire Company).

The 6 SMP-Related Hub Genes Can Serve as Diagnostic Markers for OA

To verify whether the 6 candidate hub genes can be used as clinical diagnostic markers for OA, we performed a binary univariate/multivariable logistic regression analysis for the 6 SMP-related hub genes, and statistically significant variables were included to develop a nomogram line chart for predicting the onset of OA. The nomogram shows that the possibility of OA was determined by the total scores corresponding to the sum of 6 included hub genes (Figure 3B, nomogram for 1 of the random samples). Then, the ROC curve was drawn to verify the reliability of the constructed diagnostic model. The model has a good diagnostic value for OA (Figure 3C, AUC=0.976). The random bootstrap procedure drawn with replacement from the OA samples (Figure 3C, n=100 bootstraps) was applied to assess the reliability of the constructed model. The calibration curves revealed that the model predictions of the nomogram plots were close to those of the ideal model (Figure 3C). The decision curve analysis revealed that the diagnostic model that included the 6 hub genes worked better for OA patients than the model that included only 1 hub gene (Figure 3D). Figure 4 shows the range of AUC, sensitivity, and specificity in the diagnostic model.

Figure 4.

Figure 4

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Distribution of AUC (A), sensitivity (B), and specificity (C) in repeating sample procedure. Drawn by AI (Adobe Illustrator CC2021, v25.0.0.60 for Mac version, Adobe Corporation) and R (version 4.2.1, JJ Allaire Company).

Four Hub Genes Were Identified in Peripheral Blood Samples in OA

To determine whether the diagnostic marker we identified have clinical practicality, we assessed the expression levels of the 6 hub genes. We acquired a dataset comprising transcriptome data from 26 healthy individuals and 46 OA patients’ peripheral blood samples. Upon comparing the expression levels of 4 key genes in the peripheral blood of healthy subjects and those with OA, we observed significant disparities in the expression of these genes (Figure 5). Thus, the following 4 nominated hub genes were considered hub genes of SMP in OA synovium: beta-1,3-N-acetylgalactosaminyltransferase1 (B3GALNT1), sphingosine-1-phosphate phosphatase 1 (SGPP1), sphingomyelin synthase 1 (SGMS1), and sphingosine kinase 1 (SPHK1).

Figure 5.

Figure 5

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Four hub genes identified by peripheral blood-related database (GSE63359). Differentially expression between OA and healthy synovium for B3GALNT1, SGPP1, SGMS1, SPHK1, SGPL1, and HEXB, respectively. * P<0.05, ** P<0.01. Drawn by R (version 4.2.1, JJ Allaire Company), AI (Adobe Illustrator CC2021, v25.0.0.60 for Mac version, Adobe Corporation) and GraphPad Prism (Version 8.0, GraphPad Corporation).

Hub Genes Regulated the Immune Microenvironment in OA Synovium

CIBERSORT analysis [35] was utilized to deconvolve the gene expression profiles of hub genes. Subsequently, adhering to the operational guidelines outlined on the CIBERSORT website (https://cibersortx.stanford.edu/), an mRNA-based CIBERSORT algorithm incorporating 100 permutations was created to investigate the relationship between the expression levels of the 4 SMP hub genes, immune cell infiltration, and inflammatory factors (Figure 6). Our findings revealed significant associations for B3GALNT1 with 9 out of 22 immune cell infiltrations, 5 out of 9 immune cell populations, and 13 out of 24 inflammatory cytokine levels. Additionally, we observed clear correlations between the expression level of SGMS1 and 7 out of 22 immune cell infiltrations, 6 out of 24 inflammatory cytokine levels, and a positive relationship with B lineage abundance. In the case of SPHK1, we found significant correlations with 5 out of 22 immune cell infiltrations, 9 out of 24 inflammatory cytokine levels, and the abundance of 2 out of 9 immune cell populations. Furthermore, the expression level of SGPP1 was associated with 4 out of 22 immune cell infiltrations, and 4 out of 24 inflammatory cytokine levels, and showed a negative correlation with endothelial cell abundance.

Figure 6.

Figure 6

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The correlation between 4 hub genes expression and immune model. (A) Correlation between hub genes expression and 22 immune-related cell infiltration (CIBERSORT). (B) Correlation between hub genes expression and the abundance of 9 immune-related cells (MCP-counter). (C) Correlation between hub genes expression and the level of 24 inflammatory cytokines. * P<0.05, ** P<0.01, *** P<0.001. Drawn by AI (Adobe Illustrator CC2021, v25.0.0.60 for Mac version, Adobe Corporation) and R (version 4.2.1, JJ Allaire Company).

Comparison of Clinic Parameters and Immune Characteristics Among Clusters

The connection between identified hub genes and clinical factors was explored in R software with “ggpubr” and “ConsensusClusterPlus” packages [36]. We identified the 3 subtypes of OA patents based on the optimal K value (Figure 7A). We found the expression of 4 hub genes among 3 clusters was significantly heterogeneous, as were the clinical factors containing age and sex in OA samples (Figure 7B). There was no significant association between numbers and age, while there was a significant difference in the probability of different age periods among the 3 clusters (Figure 7C, 7D). The expressions of B3GALNT1, SGPP1, CSF3, and IL2 were upregulated, while SPHK1, PDGFA, and IL1A were downregulated in cluster 2 (Figure 8A, 8B). In cluster 2, there were fewer monocytes, but there were more T cells, cytotoxic lymphocytes, myeloid dendritic cells, and endothelial cells (Figure 8C).

Figure 7.

Figure 7

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Differences among 3 clusters. (A) Identified the 3 subtypes of OA patents based on the most optimal K value. (B) The heterogeneity of gene expression is related to age and gender. (C) Correlation between age and 3 clusters. (D) Differences in the age-related possibility of OA among 3 clusters. Drawn by AI (Adobe Illustrator CC2021, v25.0.0.60 for Mac version, Adobe Corporation) and R (version 4.2.1, JJ Allaire Company).

Figure 8.

Figure 8

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Correlation between different clustering and hub gene expression and immune module. (A) Differential expression of hub genes in 3 clusters. (B) Differential express level of inflammatory cytokines in 3 clusters. (C) The abundance of 8 immune-related cells differs among their clusters. * P<0.05, ** P<0.01, *** P<0.001. Drawn by AI (Adobe Illustrator CC2021, v25.0.0.60 for Mac version, Adobe Corporation) and R (version 4.2.1, JJ Allaire Company).

Construction of Co-Expression Patterns

No samples were removed after performing quality checks among the 40 OA samples. By employing a power function (beta=10; Figure 9A), an adjacency matrix was created from a pairwise gene correlation matrix, initially computed from individual gene residual values. Subsequently, this matrix was converted into a topological overlap matrix, facilitating module assignments through the utilization of a dynamic tree cutting technique. Using the WGCNA algorithm, we identified all co-expression modules with statistical significance using optimal dynamic tree cut (Figure 9B) and hierarchical clustering (Figure 9C). The MEred modules and cluster 2 had the highest absolute correlation values, and the most relevant molecular changes of cluster 2 were detected in MEred modules. Thus, enrichment analysis was performed using genes from the MEred modules (Table 2). We observed a negative association between the phospholipase C-activating G protein-coupled receptor signaling pathway, signaling receptor activator activity, receptor ligand activity, and cluster 1. Conversely, these factors showed a positive association with cluster 2. No statistically significant difference was observed in cluster 3 (Figure 9D).

Figure 9.

Figure 9

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Identification of key modules correlated with hub genes through WGCNA. (A) Analysis of the scale-free fit index (β) and the mean connectivity for various soft-thresholding powers. (B) Clustering dendrogram of genes based on topological overlapping. (C) Heatmap of the correlation between module eigengenes and 3 clusters. (D) Functional enrichment analysis for 3 clusters that most likely enriched in MEred model. Drawn by AI (Adobe Illustrator CC2021, v25.0.0.60 for Mac version, Adobe Corporation) and R (version 4.2.1, JJ Allaire Company).

Table 2.

The results of GO/KEGG enrichment analysis of MEred in cluster 2.

Ontology ID Description Gene ratio Bg ratio p.value p.adjust
BP GO: 0044062 Regulation of excretion 4/112 24/18800 1.16e-05 0.0247
BP GO: 0007200 Phospholipase C-activating G protein-coupled receptor signaling pathway 6/112 104/18800 3.69e-05 0.0304
BP GO: 0098801 Regulation of renal system process 4/112 33/18800 4.27e-05 0.0304
BP GO: 0010660 Regulation of muscle cell apoptotic process 5/112 81/18800 0.0001 0.0346
BP GO: 0042310 Vasoconstriction 5/112 82/18800 0.0001 0.0346
MF GO: 0001664 G protein-coupled receptor binding 9/112 288/18410 6.66e-05 0.0190
MF GO: 0048018 Receptor ligand activity 11/112 489/18410 0.0002 0.0209
MF GO: 0030546 Signaling receptor activator activity 11/112 496/18410 0.0002 0.0209
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Discussion

OA is the most common degenerative osteoarthritic disease; it causes irreversible cartilage destruction and synovial inflammation [1]. In recent years, there have been increasing numbers of studies showing that synovitis plays a vital role in the pathogenic process of OA [8]. In this study, a total of 11 314 DEGs were identified between osteoarthritis (OA) and healthy synovium tissue. Among these, 19 genes were found to overlap (15 upregulated, 4 downregulated) and were associated with OA and SMP. Notably, B3GALNT1, SGPP1, SGMS1, and SPHK1 were identified as pivotal hub genes. Utilizing these hub genes, an OA risk prediction model was constructed, demonstrating their potential utility as diagnostic markers for OA. Additionally, the expression levels of these hub genes showed correlations with immune infiltration and inflammatory factors. Through unsupervised clustering and WGCNA analysis, heterogeneity in GO/KEGG enrichment was observed across different clusters.

Previous studies have shown that the triacylceramide 3-β-N-acetylgalactosamine transferase encoded by B3GALNT1 can encode glycosides and P antigens elicit immune responses [37]. Qin et al found that B3GALNT1 gene expression can regulate immune response [38]. In the present study, we found that B3GALNT1 expression shows a strong positive correlation with B lineage, memory B cells, and plasma cells, while it was negatively correlated with the abundance of naive B cells in OA. The result of B3GALNT1 expression may indicate the increased consumption of naive B cells and the activation of B cells in OA synovium. This observation suggests that the upregulation of B3GALNT1 in OA synovium can induce synovitis by enhancing the transformation of naive B cells into memory B cells and plasma cells. Furthermore, B3GALNT1 upregulates the level of IL15 and aggravates bone destruction [39], which is consistent with our results. Thus, downregulated B3GALNT1 expression may improve OA synovitis, but the specific mechanisms need further exploration. For SGPP1, previous studies reported that SGPP1 expression promotes the infiltration of immune cells [40] and the release of pro-inflammation factors [41]. Some pro-inflammation factors, such as IL-6, IL-1A, and IL-1B, can aggravate the development of local inflammation response of OA synovium [42]. In the present study, we found that the level of SGPP1 was negatively correlated with the level of IL1A but was positively correlated with TGFB1, TGFB2, and CSF3. Thus, SGPP1 expression may regulate synovitis development due to releasing different cytokines. Interestingly, SGPP1 increased the proliferation of rheumatoid arthritis (RA) synovial endothelial cells [43], but decreased the number of OA synovial endothelial cells. This inconsistency may indicate the different pathogeneses of OA and RA. The level of SGMS1 involves the release of inflammatory cytokines [44]. In the present study, SGMS1 expression was negatively correlated with IL6. In addition, it has been reported that IL6 accelerates cartilage matrix destruction [42]. Thus, the level of SGMS1 may be protective against OA injury. Previous studies found that SPHK1 regulates various immune cell functions and inflammatory mediator levels, such as IL6 and TGFB2 [45, 46]. In present study, SPHK1 level was positively correlated with IL-6 level and endothelial cell amount, but was negatively correlated with TGFB2. These results may indicate that SPHK1 can decrease the IL6 level and reduce the number of synovial endothelial cells. Meanwhile, consistent with a previous report, TGFB2 can inhibit the inflammatory response and alleviate OA progression [46]. Notable, the large confidence interval observed in the multifactorial analysis of B3GALNT1, SGMS1, and SGPL1 may be attributed to the relatively small sample size included in this study and confounding factors or unmeasured variables that were not accounted for in our analysis.

Early diagnosis plays a crucial role in timely intervention for osteoarthritis. Utilizing transcriptome data from various pathological tissues within joints, numerous studies have identified a range of biomarkers (hub genes) for early OA detection. For example, Xinyue et al [47] employed diverse bioinformatics screening methods to pinpoint key genes showing distinct expression in OA synovial tissue, suggesting their potential as diagnostic markers for OA. Similarly, Hannah et al [48] used single-cell analysis to highlight the significant role of the gene ZEB1, selected from single-cell transcriptome data of joint cartilage and meniscus, in the pathogenesis of OA. Previous research from our team has also indicated the involvement of genes within the arachidonic acid metabolism pathway in OA diagnosis and local inflammation within synovial tissues [23]. In this study, we found that the expression of SMP-related hub genes in synovium can serve as robust early markers for OA and are correlated with local inflammatory cell infiltration and levels of inflammatory factors, suggesting the tight correlation between the SMP-related hub genes and immune disorder in OA synovium. This provides a treatment direction of synovial immune disorders for OA in the future.

Four hub genes involved in sphingolipid metabolism showed heterogeneity among these 3 clusters. We observed that B3GALNT1 and SGPP1 were specifically upregulated in cluster 2, and both of them can be checked in OA patients’ peripheral blood. Thus, the B3GALNT1 and SGPP1 genes may serve as accessible potential biomarkers for OA diagnosis, especially in the elderly population. Utilizing peripheral blood testing for diagnosing OA presents the advantages of convenience and cost-effectiveness, highlighting the importance of gathering additional peripheral blood samples for validation in future studies. The relative score of T cells, endothelial cells, myeloid dendritic cells, and cytotoxic lymphocytes were upregulated, while monocytic lineage was downregulated in cluster 2. This indicates that elderly patients with OA may show a higher level of synovial vascularization and immune cell disorder [49]. We also found that CSF3 and IL2 were upregulated in cluster 2 and PDGFA was downregulated. Downregulation of CSF3 and IL2 significantly reduces the production of inflammatory markers [50,51]. Low levels of CSF3 and IL2 can relieve intractable chronic pain [52–54], and high levels of PDGFA can improve the development of OA synovitis [55]. These results may indicate that downregulation of CSF3 and IL2 and upregulation of PDGFA are potential therapeutic targets for cluster 2 (elderly OA patients). This study offers a direction for personalized treatment of osteoarthritis patients in the future, emphasizing the importance of addressing local immune dysregulation in elderly individuals with OA.

WGCNA analysis showed that MEred module eigengenes were negatively correlated with cluster 1 and cluster 2. MEred module eigengenes mainly involve the “phospholipase C-activating G protein-coupled receptor signaling pathway” in the biological process (BP). The opposite process may indicate higher activation of phospholipase C in cluster 2 compared with cluster 1. Interestingly, the activation of phospholipase C was proved to be harmful in OA, and inhibition of phospholipase C-mediated autophagy may benefit OA cartilage protection [56]. Therefore, targeting the activation of phospholipase C may be a sensitive intervention in cluster 2 patients. Meanwhile, the treatment strategies for inhibiting the activity of phospholipase C in cluster 1 patients may be ineffective. Therefore, the “phospholipase C-activating G protein-coupled receptor signaling pathway” should receive more attention in future research on treatment of elderly OA patients.

The study has limitations that need to be acknowledged. The influence of additional variables affecting the transcription level of inflammatory genes in OA synovium, such as corticosteroids, NSAIDs, and disease duration, remains unexplored. While the study proposed that SMP genes could potentially function as diagnostic markers for OA and interact with immune-mediated inflammation in the synovium, further validation through animal and clinical studies is crucial.

Conclusions

We conducted an investigation into the impact of sphingolipid metabolism in osteoarthritis (OA) synovium and observed significant upregulation of SMP genes. Among these, 4 hub genes (B3GALNT1, SGPP1, SGMS1, and SPHK1) identified from peripheral blood exhibit potential as novel diagnostic markers in OA patients. These hub genes showed significant correlations with inflammatory factors and immune-related cells. Additionally, our findings revealed differential expression of SMP hub genes among different clusters of OA patients, along with variations in the infiltration of immune-related cells and the level of inflammatory cytokines across the 3 clusters. This study suggests that SMP may play a role in regulating the immune microenvironment in OA. The participation of SMP-related hub genes in local synovial tissue immune dysregulation could be a pathogenic mechanism of OA, and targeting these hub genes may offer a potential solution to alleviate joint inflammation. However, further research is needed to fully elucidate the underlying mechanisms involved.

Data Sharing Statement

The datasets used and/or analyzed during the current study are available from the corresponding authors upon reasonable request.

Acknowledgments

We thank all the people who helped perform this study. Co-first authors Zheng Zhu and Bizhi Tu contributed equally to this paper.

Footnotes

Conflict of interest: None declared

Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher

Ethics Approval and Consent to Participate: Ethics approval for our study was granted by The Committee on Medical Ethics of The Third Affiliated Hospital of Anhui Medical University (Reference number 2022 [69]). Since all the data used in the current study was available online, and no individual patient was involved, it could be confirmed we have obtained all the written informed consent.

Declaration of Figures’ Authenticity: All figures submitted have been created by the authors who confirm that the images are original with no duplication and have not been previously published in whole or in part.

Financial support: This study was supported by Grants from the Anhui Key Clinical Specialty Construction Project, Graduate Research and Practice Innovation Project of Anhui Medical University (YJS20230198), and The Basic and Clinical Cooperative Research Promotion Program of the Third Affiliated Hospital of Anhui Medical University (2022sfy007)

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